Digital microscopic image application (DMIA), an automatic method for particle size distribution analysis in waste...

Area of Science:

• Environmental Science
• Analytical Chemistry
• Biotechnology

Background:

• Particle size distribution is a critical physical property in environmental samples.
• Conventional methods like laser diffraction are accurate but expensive and complex.
• There is a need for more accessible particle size analysis techniques.

Purpose of the Study:

• To develop and validate a digital microscopic imaging application (DMIA) for determining particle size distribution.
• To adapt existing algorithms and integrate neural network (NN) and Bayesian approaches for enhanced accuracy.
• To offer a cost-effective and less complex alternative to traditional methods.

Main Methods:

• A digital microscopic imaging application (DMIA) was developed using algorithms from helminth egg detection software.
• The DMIA incorporated neural network (NN) and Bayesian algorithms for particle size analysis.
• The method was applied to waste activated sludge (WAS), recirculated sludge (RCS), and pre-treated sludge (PTS) samples.

Main Results:

• The DMIA successfully determined particle size distribution in various sludge samples.
• Recirculation and electro-oxidation pre-treatment significantly reduced particle size and increased solubilization.
• DMIA results, particularly the 90th percentile of equivalent diameter, showed good agreement with laser diffraction measurements.

Conclusions:

• The developed DMIA provides a viable, less complex, and more economical alternative to laser diffraction for particle size analysis.
• The method demonstrates the effectiveness of integrating digital imaging with advanced algorithms for environmental sample characterization.
• Pre-treatment processes significantly impact sludge particle characteristics, which can be effectively monitored by DMIA.